Sentiment Analysis: Measuring Opinions
Procedia Computer SciencePublished 1 January 2015Open access
Chetashri Bhadane, Hardi Dalal, Heenal Doshi
Citations150
SJR score0.47
SNIP0.90
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper focuses on the various methods used for classifying a given piece of natural language text according to the opinions expressed in it i.e. whether the general attitude is negative or positive.
Abstract
Sentiment Analysis is an area of study within Natural Language Processing that is concerned with identifying the mood or opinion of subjective elements within a text. This paper focuses on the various methods used for classifying a given piece of natural language text according to the opinions expressed in it i.e. whether the general attitude is negative or positive. We also discuss the two-step method (aspect classification followed by polarity classification) that we followed along with the experimental setup.
Keywords
Computer Science
ACM Transactions on Intelligent Systems and TechnologyLIBSVM
41,340 Citations2011Chih-Chung Chang, Chih‐Jen Lin
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
arXiv (Cornell University)Thumbs up? Sentiment Classification using Machine Learning Techniques
2,208 Citations2002Bo Pang, Lillian Lee +1 more
ACM Transactions on Information SystemsMeasuring praise and criticism
1,509 Citations2003Peter D. Turney, Michael L. Littman
This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words, based on two different statistical measures of word association.
Management ScienceYahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
1,442 Citations2007Sanjiv Ranjan Das, Mike Y. Chen
A methodology for extracting small investor sentiment from stock message boards is developed, which comprises different classifier algorithms coupled together by a voting scheme.
Towards answering opinion questions
1,051 Citations2003Hong Yu, Vasileios Hatzivassiloglou
A Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories is presented, and three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level are described.
Computational IntelligenceSENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS
750 Citations2006Alistair Kennedy, Diana Inkpen
It is shown that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification, and combining the two methods achieves better results than either method alone.
Effects of adjective orientation and gradability on sentence subjectivity
671 Citations2000Vasileios Hatzivassiloglou, Janyce Wiebe
A novel trainable method that statistically combines two indicators of gradability is presented and evaluated, complementing existing automatic techniques for assigning orientation labels.
Using WordNet to Measure Semantic Orientations of Adjectives
647 Citations2004Jaap Kamps, M. Marx +2 more
Lecture notes in computer scienceA Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs
172 Citations2008Michelle Annett, Grzegorz Kondrak
A novel approach based on Support Vector Machines is proposed to classify a subset of documents using polarity metrics by applying it to a publicly available set of movie reviews.
Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters
124 Citations2005Alistair Kennedy, Diana Inkpen
It is shown that including contextual valence shifters improves the accuracy of the classification of sentiment expressed by a customer review, as well as taking both negations and intensifiers into account.
All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs.
30 Citations2007Alina Andreevskaia, Sabine Bergler +1 more
This paper explores the properties of two most common subgenres of blogs – personal diaries and “notebooks” – and the effects that these properties have on performance of an automatic sentiment annotation system, which was developed for binary and ternary classification of sentiment at the sentence level.